System and method for well log repeatability verification

ABSTRACT

A method to perform a field operation with well log repeatability verification is disclosed. The method includes generating, by repeatedly performing well logging of a wellbore penetrating a subterranean formation in a field, a set of well log data files each comprising a plurality of data channels, each data channel comprising a series of measurement data records representing a downhole property along a depth in the wellbore, analyzing, by a computer processor, a main log and a repeat log of the set of well log data files to determine a repeatability measure of the set of well log data files, presenting, using a graphical user interface, the repeatability measure to a user, and facilitating, based on a user input in response to presenting the repeatability measure, the field operation.

BACKGROUND

The term “well log” refers to measurement versus depth of physicalproperties in or around a well. The term comes from the word “log” usedin the sense of a record or a note. Wireline logs are obtained downholeand transmitted through a wireline to surface and recorded. Similarly,measurements-while-drilling (MWD) and logging while drilling (LWD) logsare also obtained downhole. Well logs are either transmitted to surfaceby mud pulses, or recorded downhole and retrieved later when the logginginstrument is brought to surface. Mud logs that describe samples ofdrilled cuttings are taken and recorded on surface.

SUMMARY

In general, in one aspect, the invention relates to a method to performa field operation with well log repeatability verification. The methodincludes generating, by repeatedly performing well logging of a wellborepenetrating a subterranean formation in a field, a set of well log datafiles each comprising a plurality of data channels, each data channelcomprising a series of measurement data records representing a downholeproperty along a depth in the wellbore, analyzing, by a computerprocessor, a main log and a repeat log of the set of well log data filesto determine a repeatability measure of the set of well log data files,presenting, using a graphical user interface, the repeatability measureto a user, and facilitating, based on a user input in response topresenting the repeatability measure, the field operation.

In general, in one aspect, the invention relates to a data gathering andanalysis system. The data gathering and analysis system includes acomputer processor and memory storing instructions, when executed,causing the computer processor to generate, by repeatedly performingwell logging of a wellbore penetrating a subterranean formation in afield, a set of well log data files each comprising a plurality of datachannels, each data channel comprising a series of measurement datarecords representing a downhole property along a depth in the wellbore,analyze a main log and a repeat log of the set of well log data files todetermine a repeatability measure of the set of well log data files,present, using a graphical user interface, the repeatability measure toa user, and facilitate, based on a user input in response to presentingthe repeatability measure, the field operation.

In general, in one aspect, the invention relates to a system. The systemincludes a wellsite having a wellbore penetrating a subterraneanformation in a field, and a data gathering and analysis systemcomprising functionality for generating, by repeatedly performing welllogging of a wellbore penetrating a subterranean formation in a field, aset of well log data files each comprising a plurality of data channels,each data channel comprising a series of measurement data recordsrepresenting a downhole property along a depth in the wellbore,analyzing, by a computer processor, a main log and a repeat log of theset of well log data files to determine a repeatability measure of theset of well log data files, presenting, using a graphical userinterface, the repeatability measure to a user, and facilitating, basedon a user input in response to presenting the repeatability measure, thefield operation.

Other aspects and advantages of the claimed subject matter will beapparent from the following description and the appended claims.

BRIEF DESCRIPTION OF DRAWINGS

Specific embodiments of the disclosed technology will now be describedin detail with reference to the accompanying figures. Like elements inthe various figures are denoted by like reference numerals forconsistency.

FIGS. 1A-1B show a system in accordance with one or more embodiments.

FIG. 2 shows a method flowchart in accordance with one or moreembodiments.

FIGS. 3A-3C show an example in accordance with one or more embodiments.

FIG. 4 shows a computing system in accordance with one or moreembodiments.

DETAILED DESCRIPTION

In the following detailed description of embodiments of the disclosure,numerous specific details are set forth in order to provide a morethorough understanding of the disclosure. However, it will be apparentto one of ordinary skill in the art that the disclosure may be practicedwithout these specific details. In other instances, well-known featureshave not been described in detail to avoid unnecessarily complicatingthe description.

Throughout the application, ordinal numbers (for example, first, second,third) may be used as an adjective for an element (that is, any noun inthe application). The use of ordinal numbers is not to imply or createany particular ordering of the elements nor to limit any element tobeing only a single element unless expressly disclosed, such as usingthe terms “before”, “after”, “single”, and other such terminology.Rather, the use of ordinal numbers is to distinguish between theelements. By way of an example, a first element is distinct from asecond element, and the first element may encompass more than oneelement and succeed (or precede) the second element in an ordering ofelements.

In general, embodiments of the disclosure include systems and methodsfor performing a field operation as facilitated by well logrepeatability verification. The field operation refers to physicalactivities performed in an oil and gas field. By performing well loggingof a wellbore penetrating a subterranean formation in a field, a welllog data file is generated that includes a large number of datachannels, each data channel having a series of measurement data recordsrepresenting a downhole property (e.g., irradiation, density, electricaland acoustic properties, etc.) along a depth in the wellbore. Using acomputer processor, a main log and a repeat log from repeatedlyperformed well loggings are analyzed to generate a repeatability summaryreport that includes a repeatability measure of the set of well log datafiles. The repeatability summary report is presented using a graphicaluser interface (GUI) to a user. The field operation (e.g., wellproduction operation, well drilling operation, well completionoperation, well maintenance operation, reservoir monitoring, assessmentand development operation, etc.) is then advantageously performed basedon a user input in response to presenting the verification summary.

FIG. 1A shows a schematic diagram of a well environment in accordancewith one or more embodiments. In one or more embodiments, one or more ofthe modules and/or elements shown in FIG. 1A may be omitted, repeated,and/or substituted. Accordingly, embodiments disclosed herein should notbe considered limited to the specific arrangements of modules and/orelements shown in FIG. 1A.

As shown in FIG. 1A, a well environment (100) includes a subterraneanformation (“formation”) (104) and a well system (106). The formation(104) may include a porous or fractured rock formation that residesunderground, beneath the earth's surface (“surface”) (108). Theformation (104) may include different layers of rock having varyingcharacteristics, such as varying degrees of permeability, porosity,capillary pressure, and resistivity. In the case of the well system(106) being a hydrocarbon well, the formation (104) may include ahydrocarbon-bearing reservoir (102). In the case of the well system(106) being operated as a production well, the well system (106) mayfacilitate the extraction of hydrocarbons (or “production”) from thereservoir (102).

In some embodiments disclosed herein, the well system (106) includes arig (101), a wellbore (120), a data gathering and analysis system (160),and a well control system (“control system”) (126). The well controlsystem (126) may control various operations of the well system (106),such as well production operations, well drilling operation, wellcompletion operations, well maintenance operations, and reservoirmonitoring, assessment and development operations. In some embodiments,the well control system (126) includes a computer system.

The rig (101) is the machine used to drill a borehole to form thewellbore (120). Major components of the rig (101) include the drillingfluid tanks, the drilling fluid pumps (e.g., rig mixing pumps), thederrick or mast, the draw works, the rotary table or top drive, thedrill string, the power generation equipment and auxiliary equipment.Drilling fluid, also referred to as “drilling mud” or simply “mud,” isused to facilitate drilling boreholes into the earth, such as drillingoil and natural gas wells.

In some embodiments, a bottom hole assembly (BHA) (151) is attached tothe drill string (150) to suspend into the wellbore (120) for performingthe well drilling operation. The bottom hole assembly (BHA) is thelowest part of the drill string (150) and includes the drill bit, drillcollar, stabilizer, mud motor, etc.

The wellbore (120) includes a bored hole (i.e., borehole) that extendsfrom the surface (108) towards a target zone of the formation (104),such as the reservoir (102). The wellbore (120) may be drilled forexploration, development and production purposes. The wellbore (120) mayfacilitate the circulation of drilling fluids during drilling operationsfor the wellbore (120) to extend towards the target zone of theformation (104) (e.g., the reservoir (102)), facilitate the flow ofhydrocarbon production (e.g., oil and gas) from the reservoir (102) tothe surface (108) during production operations, facilitate the injectionof substances (e.g., water) into the hydrocarbon-bearing formation (104)or the reservoir (102) during injection operations, or facilitate thecommunication of logging tools lowered into the formation (104) or thereservoir (102) during logging operations. The wellbore (120) may belogged by lowering a combination of physical sensors downhole to acquiredata that measures various rock and fluid properties, such asirradiation, density, electrical and acoustic properties. The acquireddata may be organized in a log format and referred to as well logs orwell log data.

In some embodiments, the data gathering and analysis system (160)includes hardware and/or software with functionality for facilitatingoperations of the well system (106), such as well production operations,well drilling operation, well completion operations, well maintenanceoperations, and reservoir monitoring, assessment and developmentoperations. For example, the data gathering and analysis system (160)may store drilling data records of drilling the wellbore (120) and welllog data records of logging the wellbore (120). The data gathering andanalysis system (160) may validate and analyze the well log data recordsto generate recommendations to facilitate various operations of the wellsystem (106). In particular, the well log data records are validatedbefore analysis. Once validated, a reservoir simulator may be used tofurther analyze the validated well log data and/or other types of datato generate and/or update reservoir models. The verification aspect ofthe data gathering and analysis system (160) is referred to as a welllog verification system. While the data gathering and analysis system(160) is shown at a well site, embodiments are contemplated where atleast a portion of the data gathering and analysis system (160) islocated away from well sites. In some embodiments, the data gatheringand analysis system (160) may include a computer system that is similarto the computer system (400) described below with regard to FIG. 4 andthe accompanying description.

FIG. 1B shows details of the data gathering and analysis system (160)depicted in FIG. 1A above, in accordance with one or more embodimentsdisclosed herein. In one or more embodiments, one or more of the modulesand/or elements shown in FIG. 1B may be omitted, repeated, and/orsubstituted. Accordingly, embodiments disclosed herein should not beconsidered limited to the specific arrangements of modules and/orelements shown in FIG. 1B.

As shown in FIG. 1B, the data gathering and analysis system (160) hasmultiple components, including, for example, a buffer (114), averification configuration engine (111), a repeatability check engine(112), and a verification display engine (113). Each of these componentsis discussed below.

In one or more embodiments, the buffer (114) may be implemented inhardware (i.e., circuitry), software, or any combination thereof. Thebuffer (114) may be any data structure configured to store input data,output results, and intermediate data of the verification configurationengine (111), the repeatability check engine (112), and the verificationdisplay engine (113). In one or more embodiments, the buffer (114)stores well log data files (115), a verification configuration file(116), depth log difference (117), correlation coefficient without depthshift (118), a bulk histogram (119), depth shift (120), and depth shiftdependent correlation coefficient (121).

The well log data files (115) include wireline logs, MWD logs, LWD logs,mud logs, etc. of one or more wells in a field. Each wireline log, MWDlog, LWD log, mud log, etc. may be repeated multiple times under samedownhole conditions to verify repeatability associated with, e.g.,wellbore environment, sensor noise, measurement instrument, etc. In oneor more embodiments, within the multiple logs, the initial log isreferred to as the main log and one or more subsequent log(s) arereferred to as repeat log(s). Because the repeat log may not be an exactcopy of the main log due to sensor noise and/or measurement instrumentreliability, etc., wireline logs are often acquired in a repeat passalong a short interval (e.g., a depth interval of 100 m or severalhundred feet) to validate the main run data and to make sure the logresponse is repeatable under same downhole conditions. This is a qualitycontrol over the tool functionality and reliability to generaterepeatable log. That is, how well a log can be repeated or reproducedalong this short interval is often used as important criteria for dataquality, especially across zones with special response, known response,log anomaly, etc. In one or more embodiments, the degree of how well alog can be repeated or reproduced under same downhole conditions ismeasured by a correlation coefficient and associated statisticparameters between the main log and the repeat log. The correlationcoefficient and associated statistical parameters are referred to as arepeatability measure, which may be limited by the downhole toolfunctionality and reliability. In one or more embodiments, the well logdata files (115) are archived in DLIS (Digital Log Information Standard)or LAS (Log ASCII Standard) files as deliverables from a logging serviceprovider to an operating entity of the field, such as a drillingcompany, an oil/gas production company, etc. generally referred to asthe operating company.

The verification configuration file (116) is a configuration file thatspecifies various criteria for determining repeatability of the well logdata files. In one or more embodiments, the criteria describe PASS/FAILconditions for a strict test of repeatability. The strict test ofrepeatability is a test that determines repeatability by comparing mainlog and repeat log without taking into account any depth shiftadjustment. In addition, the criteria further describe PASS/FAILconditions for a lenient test of repeatability. The lenient test ofrepeatability is a test that determines repeatability by comparing mainlog and repeat log while taking into account the effect of depth shiftadjustments.

The depth log difference (117) is a log of computed differences betweenthe main log and a repeat log. In one or more embodiments, the depth logdifference (117) includes percentage difference values versus the depthalong the wellbore.

The correlation coefficient without depth shift (118) is a measure thatquantifies the strength of a linear relationship between the main logand a repeat log. The measure is computed based on correspondingmeasurement values in the main log and one or more repeat logs withoutadjusting any depth values in the main log and repeat log.

The bulk histogram (119) is a set of histograms of the main log and therepeat log(s). For example, the bulk histogram (119) may include ahistogram of the main log superimposed over a histogram of the repeatlog. Both mean values and standard deviations are compared to check logrepeatability.

The depth shift (120) is an adjustment to offset the depth scale of themain log with respect to a repeat log. For example, the adjustment maycorrespond to an alignment error of depth measurements between the mainlog and the repeat log due to calibration or other instrumentationissues.

The depth shift dependent correlation coefficient (121) is the computedcorrelation coefficient versus a variable value of the depth shift(120). Specifically, the depth shift dependent correlation coefficient(121) is computed with a variable offset in the depth scales of the mainlog and the repeat log. Specifically, the depth scales of the main logand the repeat log are offset by the variable values of the depth shift(120).

In one or more embodiments, the verification configuration engine (111)may be implemented in hardware (i.e., circuitry), software, or anycombination thereof. The verification configuration engine (111) isconfigured to generate, select, and/or revise the verificationconfiguration file (116) based on user input. For example, the userinput may be received via a graphical user interface (GUI), morespecifically via selection or data entry fields of the GUI.

In one or more embodiments, the repeatability check engine (112) may beimplemented in hardware (i.e., circuitry), software, or any combinationthereof. The repeatability check engine (112) is configured to analyzethe well log data files (115) to perform a strict test of repeatabilityand a lenient test of repeatability based on respective correlationanalyses of the main log and the repeat log. Strict test refers to atest without any depth adjustments in the logs, while the lenient testallows depth adjustments to improve the correlation between the adjustedlogs. The strict test vs. the lenient test are discussed further in FIG.2 below.

In one or more embodiments, the verification display engine (113) may beimplemented in hardware (i.e., circuitry), software, or any combinationthereof. The verification display engine (113) is configured to displaythe results of the strict test of repeatability and the lenient test ofrepeatability. For example, results of the strict test of repeatabilityand the lenient test of repeatability may be displayed via a GUI to theuser.

In one or more embodiments, the verification configuration engine (111),the repeatability check engine (112), and the verification displayengine (113) collectively perform the functionalities described aboveusing the method described in reference to FIG. 2 below.

Although the data gathering and analysis system (160) is shown as havingfour components (111, 112, 113, 114), in other embodiments, the datagathering and analysis system (160) may have more or fewer components.Further, the functionality of each component described above may besplit across multiple components. Further still, each component (111,112, 113, 114) may be utilized multiple times to carry out an iterativeoperation.

FIG. 2 shows a flowchart in accordance with one or more embodimentsdisclosed herein. One or more of the steps in FIG. 2 may be performed bythe components of the well environment (100), in particular the datagathering and analysis system (160), discussed above in reference toFIGS. 1A-1B. In one or more embodiments, one or more of the steps shownin FIG. 2 may be omitted, repeated, and/or performed in a differentorder than the order shown in FIG. 2 . Accordingly, the scope of thedisclosure should not be considered limited to the specific arrangementof steps shown in FIG. 2 .

Referring to FIG. 2 , initially in Step 200, well log data files areloaded into a data gathering and analysis system. Each log data file maybe a single compressed file containing a large number of data channelsarranged in DLIS, LAS, LIS, or other suitable formats. The well log datafiles include one or more sets of main logs and repeat logs of a well.The well log data files to be loaded may be located in a local folder ofthe data gathering and analysis system or retrieved from a remote dataserver. Previously loaded well log data file cache may also be removed(i.e., flushed) or updated.

In Step 201, a verification configuration file is loaded into the datagathering and analysis system. The verification configuration filecontains a set of rules and criteria for determining whether each set ofrepeat and main logs is repeatable or not. The repeatability may bedetermined on a per service (i.e., based on all data channels in thewell log data files) or per data channel basis. The verificationconfiguration file to be loaded may be located in a local folder of thedata gathering and analysis system or retrieved from a remote dataserver. Previously loaded configuration file cache may also be removed(i.e., flushed) or updated.

In Step 202, data channels of both main and repeat logs of each set oflogs are displayed for user visualization. In one or more embodiments,the display further includes a difference between the main and repeatlogs along the depth range of the logs. An example of displaying themain and repeat logs is shown in FIG. 3A below.

In Step 203: a strict test of repeatability is performed by calculatingdepth log difference and cross correlation coefficient without depthshift. In one or more embodiments, the log difference and crosscorrelation coefficient are computed using Eqs. (1)-(3) below. Forexample, the cross-correlation coefficient is computed as Pearsoncorrelation coefficient using either Eq. (2) or Eq. (3) below.

$\begin{matrix}{{{Difference}(\%)} = {\frac{x_{repeat} - x_{main}}{x_{main}} \times 100\%}} & {{Eq}.(1)}\end{matrix}$ $\begin{matrix}{\rho_{X,Y} = {\frac{{cov}\left( {X,Y} \right)}{\sigma_{X}\sigma_{Y}} = \frac{{\mathbb{E}}\left\lbrack {\left( {X - \mu_{X}} \right)\left( {Y - \mu_{Y}} \right)} \right\rbrack}{\sqrt{{\mathbb{E}}\left\lbrack \left( {X - \mu_{X}} \right)^{2} \right\rbrack}\sqrt{{\mathbb{E}}\left\lbrack \left( {Y - \mu_{Y}} \right)^{2} \right\rbrack}}}} & {{Eq}.(2)}\end{matrix}$ $\begin{matrix}{\rho_{X,Y} = \frac{{\sum}_{i = 1}^{n}\left( {x_{i} - \mu_{X}} \right){\sum}_{i = 1}^{n}\left( {y_{i} - \mu_{Y}} \right)}{\sqrt{{\sum}_{i = 1}^{n}\left( {x_{i} - \mu_{X}} \right)^{2}}\sqrt{{\sum}_{i = 1}^{n}\left( {y_{i} - \mu_{Y}} \right)^{2}}}} & {{Eq}.(3)}\end{matrix}$

In Eqs. (1)-(3), X is the repeat log and Y is the main log. The meanvalues are defined as

${\mu_{X} = {{{\mathbb{E}}\lbrack X\rbrack} = {\frac{1}{n}{\sum}_{i = 1}^{n}x_{i}}}},{{{and}\mu_{Y}} = {{{\mathbb{E}}\lbrack Y\rbrack} = {\frac{1}{n}{\sum}_{i = 1}^{n}y_{i}}}}$

where the subscripts correspond to depth values in the logs.

In one or more embodiments, a determination is made as to whether thecomputed depth log difference and cross correlation coefficient satisfya pre-determined criterion, such as the depth log difference being lessthan a maximum value of 5% and the cross-correlation coefficient withoutdepth shift exceeding a minimum value of 0.90. If the determination ispositive, i.e., the main and repeat logs pass the strict test ofrepeatability, the method proceeds to Step 206. If the determination isnegative, i.e., the main and repeat logs fail the strict test ofrepeatability, the method proceeds to Step 204. An example histogram andcross plot of the strict test of repeatability is shown in FIG. 3Bbelow.

In Step 204, a lenient test of repeatability is performed by calculatingbulk histogram shift and plotting cross-correlation coefficient versusdepth shift. In one or more embodiments, the correlation coefficient isdefined by Zero-normalized cross-correlation coefficient (ZNCC) andcomputed using Eq. (4) below where T represents depth shift.

$\begin{matrix}{{\rho_{X,Y}(\tau)} = {\frac{{\mathbb{E}}\left\lbrack {\left( {{X\left( {t - \tau} \right)} - \mu_{X}} \right)\left( {{Y(t)} - \mu_{Y}} \right)} \right\rbrack}{\sqrt{{\mathbb{E}}\left\lbrack \left( {X - \mu_{X}} \right)^{2} \right\rbrack}\sqrt{{\mathbb{E}}\left\lbrack \left( {Y - \mu_{Y}} \right)^{2} \right\rbrack}} = \frac{{\sum}_{i = 1}^{n}\left( {x_{i - \tau} - \mu_{X}} \right){\sum}_{i = 1}^{n}\left( {y_{i} - \mu_{Y}} \right)}{\sqrt{{\sum}_{i = 1}^{n}\left( {x_{i} - \mu_{X}} \right)^{2}}\sqrt{{\sum}_{i = 1}^{n}\left( {y_{i} - \mu_{Y}} \right)^{2}}}}} & {{Eq}.(4)}\end{matrix}$

In addition, the optimal depth shift is computed using Eq. (5) below.

$\begin{matrix}{\tau_{\max} = {\underset{\tau}{\arg\max}{\rho_{X,Y}(\tau)}}} & {{Eq}.(5)}\end{matrix}$

Then the optimal cross correlation coefficient (i.e., optimal value ofthe depth shift dependent correlation coefficient) is computed using Eq.(6) below.

$\begin{matrix}{= {{\rho_{X,Y}\left( \tau_{\max} \right)} = \frac{{\sum}_{i = 1}^{n}\left( {x_{i - \tau_{\max}} - \mu_{X}} \right){\sum}_{i = 1}^{n}\left( {y_{i} - \mu_{Y}} \right)}{\sqrt{{\sum}_{i = 1}^{n}\left( {x_{i} - \mu_{X}} \right)^{2}}\sqrt{{\sum}_{i = 1}^{n}\left( {y_{i} - \mu_{Y}} \right)^{2}}}}} & {{Eq}.(6)}\end{matrix}$

Alternatively, the correlation coefficient may also be defined bycalculating the difference between main log and repeat log withalignment from phase unwrapping or dynamic warping using Eq. (7) below.

$\begin{matrix}{{\rho_{X,Y}\left\lbrack {\tau(t)} \right\rbrack} = {\sum\frac{{X\left( {t - {\tau(t)}} \right)} - {Y(t)}}{Y(t)}}} & {{Eq}.(7)}\end{matrix}$

In Eq. (7), τ(t) is the user defined alignment algorithm where trepresents depth and τ(t) represents depth shift at depth t. Thesummation Σ is over all valid depth (t) values.

In Eqs. (4)-(7), X is the repeat log and Y is the main log. The meanvalues are defined as

${\mu_{X} = {{{\mathbb{E}}\lbrack X\rbrack} = {\frac{1}{n}{\sum}_{i = 1}^{n}x_{i}}}},{{{and}\mu_{Y}} = {{{\mathbb{E}}\lbrack Y\rbrack} = {\frac{1}{n}{\sum}_{i = 1}^{n}y_{i}}}}$

where the subscripts correspond to depth values in the logs.

In one or more embodiments, a determination is made as to whether thecomputed bulk histogram shift and a depth-shift dependentcross-correlation coefficient satisfy a pre-determined criterion, suchas the bulk histogram shift being less than a maximum value, such as 10%of the mean value and the depth-shift dependent cross-correlationcoefficient exceeding a minimum value of 0.90. If the determination ispositive, i.e., the main and repeat logs pass the lenient test ofrepeatability, the method proceeds to Step 206. If the determination isnegative, i.e., the main and repeat logs fail the lenient test ofrepeatability, the method proceeds to Step 205. An example bulkhistogram shift of repeat log versus main log and an example depth-shiftdependent cross-correlation coefficient plot of the lenient test ofrepeatability are shown in FIG. 3C below.

In Step 205, a request is sent to an expert user to provide arepeatability check, when the main and repeat logs fail both the strictand lenient tests of repeatability. For example, the expert user maydecide to set the repeatability check as “failed” or relax the criteriaof bulk histogram shift and depth-shift dependent cross-correlationcoefficient to perform both the strict and lenient tests again.

In one or more embodiments, a determination is made as to whether themain and repeat logs pass the user provided repeatability check. Whetherthe determination is positive or negative, i.e., whether the main andrepeat logs pass or fail the user provided repeatability check, themethod proceeds to Step 206 with the result of the user providedrepeatability check.

In Step 206, a log repeatability summary report is generated. In one ormore embodiments, the log repeatability summary report includesquantitative metrics computed using one or more of Eqs. (1)-(7) forverifying log repeatability in Step 203, Step 204, and/or Step 205. Forexample, the log repeatability summary report may include one or morerepeatability measures, such as the repeatability PASS/FAIL status andone or more of the depth log difference, correlation coefficient withoutdepth shift, bulk histogram, optimal depth shift, and optimal value ofdepth shift dependent correlation coefficient. In one or moreembodiments, the log repeatability summary report is presented (e.g.,displayed) to the user.

In Step 208, in response to viewing and reviewing the log repeatabilitysummary report, the user may initiate and/or adjust a field operationassociated with the main and repeat logs.

FIGS. 3A-3C show an implementation example in accordance with one ormore embodiments. The implementation example shown in FIGS. 3A-3C isbased on the system and method flowchart described in reference to FIGS.1A, 1B, and 2 above. In one or more embodiments, one or more of themodules and/or elements shown in FIGS. 3A-3C may be omitted, repeated,and/or substituted. Accordingly, embodiments disclosed herein should notbe considered limited to the specific arrangements of modules and/orelements shown in FIGS. 3A-3C.

In the oil & gas and mining industry, wells are drilled for bothexploration and production purposes. Wells are commonly logged bylowering a combination of physical sensors (or sondes) downhole toacquire data that measures various rock and fluid properties, such asirradiation, density, electrical and acoustic properties. Well log dataare archived in DLIS or LAS files as deliverables. One DLIS or LAS filemay contain hundreds or thousands of data channels that are difficult tobe visualized and validated manually. Furthermore, wireline logs areoften acquired in one or more repeat pass(s) during a short interval tovalidate the main run data ensuring that the log response is repeatableunder same downhole conditions, and as a quality control to verify thetool functionality and reliability. The degree that a well log can berepeated or reproduced during this short interval is used as a criteriafor data quality, especially across target zones with special response,known response, log anomaly, etc.

DLIS or LAS well log data files have to pass data quality verificationbefore going into an operating company's database. This verificationprocess is tedious and usually takes very long time. One DLIS or LASfile may contain hundreds or thousands of data channels that aredifficult to be visualized and validated manually. From time to time,multiple iterations of communication between the logging serviceprovider and the operating company are required to achieve a final validdata deliverable. When this tedious task is performed by a human expertusing a well log interpretation software, the expert needs to visualizeand check all data channels one by one for its validity andrepeatability before summarizing all issues in a report provided to thelogging service provider as feedback before the well log files arefinalized for delivering to the operating company. Furthermore, the listof data channels is often tool specific and varies significantly amongdifferent tools. Therefore, it is not efficient and not consistent torely on human expert to complete this task.

FIGS. 3A-3C show an example of systematically tackling the issue ofverifying log data repeatability in the main and repeat log data (DLISor LAS deliverables) in an automatic and rapid manner that coversvarious types of log measurements. The example is based on an automatedsoftware system and workflow that allows (i) visualization of logs ofboth repeat and main runs, (ii) pre-defined and loaded repeatabilitycriteria per each data channel, (iii) three levels of repeatabilityverification: visual test, lenient test, and strict test, and (iv) apool of quantitative metrics of repeatability such as mean logdifference, histogram shift, peak cross-correlation coefficient formeasuring repeatability. For example, the automated software system andworkflow reduce the time to verify repeatability of one set of DLISfiles (main+repeat) that has 100 data channels from hours to justseveral minutes, and may be automated to process a large quantities oflogs with minimal human intervention.

FIG. 3A shows an example screenshot of main log and repeat log aspresented to a user for the visual test of well log repeatability. Inparticular, FIG. 3A shows, according to the legend (301), the plot of amain log (301 a), a repeat log (301 b), and difference (301 c) betweenthe main log (301 a) and repeat log (301 b). In particular, the main log(301 a) and repeat log (301 b) record gamma radiation (GR) measurementsin gapi (API gamma ray unit) versus depth in ft. The difference (301 c)records percentage differences versus depth. The display shown in FIG.3A may correspond to Step 202 depicted in FIG. 2 above.

FIG. 3B shows an example display of the result of a strict test ofrepeatability. The display shown in FIG. 3B may generated by performingStep 203 depicted in FIG. 2 above.

In particular, FIG. 3B shows, according to the legend (302), a histogram(302 a) of percentage difference between the main log (301 a) and repeatlog (301 b). In an example scenario, the mode (i.e., most frequentoccurring value) of the histogram (302 a) corresponds to a main versusrepeat log difference of 4%, which is less than a maximum thresholdvalue of 5% resulting in PASS of the strict test of repeatability. In adifferent scenario where the maximum threshold value is defined as 3%,which is exceeded by the mode of the histogram (302 a) resulting in FAILof the strict test of repeatability.

In addition, FIG. 3B shows a cross plot (302 b) of the repeat log (301b) versus the main log (301 a) without considering any depth shiftadjustment. In other words, the cross plot (302 b) is generated usingcorresponding GR values of the main log (301 a) and repeat log (301 b)without any shifting adjustment to depth values in the main log (301 a)and repeat log (301 b). In an example scenario, the lines (312 a, 312 b,312 c) superimposing the cross plot (302 b) represent the y=x line andy=x±one standard deviation lines. If more than 50% of the points arefalling out of the zone between the lines (312 b, 312 c), the stricttest fails.

FIG. 3C shows an example display of the result of a lenient test ofrepeatability. The display shown in FIG. 3C may generated by performingStep 204 depicted in FIG. 2 above.

In particular, FIG. 3C shows, according to the legend (303), bulkhistograms (303 a) and (303 b) of the main log (301 a) and the repeatlog (301 b), respectively. The vertical axis shows the probabilitydensity. The bulk histograms (303 a) and (303 b) show a bulk histogramshift (303 c) based on the median values (i.e., middle values) of therepeat log (301 b) versus the main log (301 a). A large bulk histogramshift between the main and repeat logs over the same interval indicatesa systematic measurement offset that might be caused by logging toolmalfunction. In this case, the lenient test of repeatability fails.

FIG. 3C also shows a depth-shift dependent cross-correlation coefficientplot (303 d) that is calculated using Eq. (4) above. Based on Eqs.(5)-(6), the mode (303 e) of the depth-shift dependent cross-correlationcoefficient plot (303 d) corresponds to the optimal depth shift τ_(max)of 0.5 ft and the optimal cross correlation coefficient

equal to 0.95. In other words, the cross correlation coefficient has amaximum value 0.95 when the depth shift is set to 0.5 ft in Eqs.(4)-(6). In an example scenario, the optimal cross correlationcoefficient

exceeds a minimum threshold value of 0.90 resulting in PASS of thelenient test of repeatability.

Embodiments disclosed herein provide a method for verifying log datarepeatability in the main and repeat log data (DLIS or LAS deliverables)in an automatic and rapid manner. The python-based automated softwaresystem and workflow disclosed herein allows for visualization of logs ofboth repeat and main runs, pre-defined and loaded repeatability criteriaper each data channel, and three levels of repeatability verification:visual test, lenient test, and strict test. A pool of quantitativemetrics of repeatability such as mean log difference, histogram shift,peak cross-correlation coefficient for measuring repeatability is usedto perform repeatability verification. Using embodiments disclosedherein, the time for verifying repeatability of one set of DLIS files(main+repeat) that has 100 data channels is reduced from hours tominutes.

Embodiments may be implemented on a computing system. FIG. 4 depicts ablock diagram) of a computing system (400) including a computer (402)used to provide computational functionalities associated with describedmachine learning networks, algorithms, methods, functions, processes,flows, and procedures as described in this disclosure, according to oneor more embodiments. The illustrated computer (402) is intended toencompass any computing device such as a server, desktop computer,laptop/notebook computer, wireless data port, smart phone, personal dataassistant (PDA), tablet computing device, one or more processors withinthese devices, or any other suitable processing device, including bothphysical or virtual instances (or both) of the computing device.Additionally, the computer (402) may include a computer that includes aninput device, such as a keypad, keyboard, touch screen, or other devicethat can accept user information, and an output device that conveysinformation associated with the operation of the computer (402),including digital data, visual, or audio information (or a combinationof information), or a GUI.

The computer (402) can serve in a role as a client, network component, aserver, a database or other persistency, or any other component (or acombination of roles) of a computer system for performing the subjectmatter described in the instant disclosure. The illustrated computer(402) is communicably coupled with a network (430). In someimplementations, one or more components of the computer (402) may beconfigured to operate within environments, includingcloud-computing-based, local, global, or other environment (or acombination of environments).

At a high level, the computer (402) is an electronic computing deviceoperable to receive, transmit, process, store, or manage data andinformation associated with the described subject matter. According tosome implementations, the computer (402) may also include or becommunicably coupled with an application server, e-mail server, webserver, caching server, streaming data server, business intelligence(BI) server, or other server (or a combination of servers).

The computer (402) can receive requests over network (430) from a clientapplication (for example, executing on another computer (402)) andresponding to the received requests by processing the said requests inan appropriate software application. In addition, requests may also besent to the computer (402) from internal users (for example, from acommand console or by other appropriate access method), external orthird-parties, other automated applications, as well as any otherappropriate entities, individuals, systems, or computers.

Each of the components of the computer (402) can communicate using asystem bus (403). In some implementations, any or all of the componentsof the computer (402), both hardware or software (or a combination ofhardware and software), may interface with each other or the interface(404) (or a combination of both) over the system bus (403) using anapplication programming interface (API) (412) or a service layer (413)(or a combination of the API (412) and service layer (413). The API(412) may include specifications for routines, data structures, andobject classes. The API (412) may be either computer-languageindependent or dependent and refer to a complete interface, a singlefunction, or even a set of APIs. The service layer (413) providessoftware services to the computer (402) or other components (whether ornot illustrated) that are communicably coupled to the computer (402).The functionality of the computer (402) may be accessible for allservice consumers using this service layer. Software services, such asthose provided by the service layer (413), provide reusable, definedbusiness functionalities through a defined interface. For example, theinterface may be software written in JAVA, C++, or other suitablelanguage providing data in extensible markup language (XML) format oranother suitable format. While illustrated as an integrated component ofthe computer (402), alternative implementations may illustrate the API(412) or the service layer (413) as stand-alone components in relationto other components of the computer (402) or other components (whetheror not illustrated) that are communicably coupled to the computer (402).Moreover, any or all parts of the API (412) or the service layer (413)may be implemented as child or sub-modules of another software module,enterprise application, or hardware module without departing from thescope of this disclosure.

The computer (402) includes an interface (404). Although illustrated asa single interface (404) in FIG. 4 , two or more interfaces (404) may beused according to particular needs, desires, or particularimplementations of the computer (402). The interface (404) is used bythe computer (402) for communicating with other systems in a distributedenvironment that are connected to the network (430). Generally, theinterface (404) includes logic encoded in software or hardware (or acombination of software and hardware) and operable to communicate withthe network (430). More specifically, the interface (404) may includesoftware supporting one or more communication protocols, such as theWellsite Information Transfer Specification (WITS) protocol, associatedwith communications such that the network (430) or interface's hardwareis operable to communicate physical signals within and outside of theillustrated computer (402).

The computer (402) includes at least one computer processor (405).Although illustrated as a single computer processor (405) in FIG. 4 ,two or more processors may be used according to particular needs,desires, or particular implementations of the computer (402). Generally,the computer processor (405) executes instructions and manipulates datato perform the operations of the computer (402) and any algorithms,methods, functions, processes, flows, and procedures as described in theinstant disclosure.

The computer (402) also includes a memory (406) that holds data for thecomputer (402) or other components (or a combination of both) that canbe connected to the network (430). For example, memory (406) can be adatabase storing data consistent with this disclosure. Althoughillustrated as a single memory (406) in FIG. 4 , two or more memoriesmay be used according to particular needs, desires, or particularimplementations of the computer (402) and the described functionality.While memory (406) is illustrated as an integral component of thecomputer (402), in alternative implementations, memory (406) can beexternal to the computer (402).

The application (407) is an algorithmic software engine providingfunctionality according to particular needs, desires, or particularimplementations of the computer (402), particularly with respect tofunctionality described in this disclosure. For example, application(407) can serve as one or more components, modules, applications, etc.Further, although illustrated as a single application (407), theapplication (407) may be implemented as multiple applications (407) onthe computer (402). In addition, although illustrated as integral to thecomputer (402), in alternative implementations, the application (407)can be external to the computer (402).

There may be any number of computers (402) associated with, or externalto, a computer system containing a computer (402), wherein each computer(402) communicates over network (430). Further, the term “client,”“user,” and other appropriate terminology may be used interchangeably asappropriate without departing from the scope of this disclosure.Moreover, this disclosure contemplates that many users may use onecomputer (402), or that one user may use multiple computers (402).

While the invention has been described with respect to a limited numberof embodiments, those skilled in the art, having benefit of thisdisclosure, will appreciate that other embodiments can be devised whichdo not depart from the scope of the disclosure as disclosed herein.Accordingly, the scope of the disclosure should be limited only by theattached claims.

What is claimed:
 1. A method to perform a field operation with well logrepeatability verification, comprising: generating, by repeatedlyperforming well logging of a wellbore penetrating a subterraneanformation in a field, a set of well log data files each comprising aplurality of data channels, each data channel comprising a series ofmeasurement data records representing a downhole property along a depthin the wellbore; analyzing, by a computer processor, a main log and arepeat log of the set of well log data files to determine arepeatability measure of the set of well log data files; presenting,using a graphical user interface, the repeatability measure to a user;and facilitating, based on a user input in response to presenting therepeatability measure, the field operation.
 2. The method of claim 1,further comprising: obtaining, using the graphical user interface, averification configuration file, wherein determining the repeatabilitymeasure of the set of well log data files is based on the verificationconfiguration file.
 3. The method of claim 1, wherein analyzing the mainlog and the repeat log comprises: performing a strict test ofrepeatability based on a first correlation analysis of the main log andthe repeat log without any depth shift adjustment, wherein said firstcorrelation analysis generates, as part of the repeatability measure, acorrelation coefficient without depth shift.
 4. The method of claim 3,further comprising: determining, by at least comparing the correlationcoefficient without depth shift to a first pre-determined minimumcorrelation threshold, a pass status or a fail status of the strict testof repeatability, wherein the repeatability measure further comprisesthe pass status or the fail status of the strict test of repeatability.5. The method of claim 4, wherein analyzing the main log and the repeatlog further comprises: performing, in response to determining the failstatus of the strict test of repeatability, a lenient test ofrepeatability based on a second correlation analysis of the main log andthe repeat log with an optimal depth shift adjustment, wherein saidsecond correlation analysis generates, as part of the repeatabilitymeasure, a depth shift dependent correlation coefficient.
 6. The methodof claim 5, further comprising: determining, by at least comparing thedepth shift dependent correlation coefficient to a second pre-determinedminimum correlation threshold, a pass status or a fail status of thelenient test of repeatability, wherein the repeatability measure furthercomprises the pass status or the fail status of the lenient test ofrepeatability.
 7. The method of claim 5, wherein the optimal depth shiftadjustment corresponds to an optimal value of the depth shift dependentcorrelation coefficient.
 8. A data gathering and analysis system,comprising: a computer processor; and memory storing instructions, whenexecuted, causing the computer processor to: generate, by repeatedlyperforming well logging of a wellbore penetrating a subterraneanformation in a field, a set of well log data files each comprising aplurality of data channels, each data channel comprising a series ofmeasurement data records representing a downhole property along a depthin the wellbore; analyze a main log and a repeat log of the set of welllog data files to determine a repeatability measure of the set of welllog data files; present, using a graphical user interface, therepeatability measure to a user; and facilitate, based on a user inputin response to presenting the repeatability measure, the fieldoperation.
 9. The data gathering and analysis system of claim 8, theinstructions, when executed, further causing the computer processor to:obtain, using the graphical user interface, a verification configurationfile, wherein determining the repeatability measure of the set of welllog data files is based on the verification configuration file.
 10. Thedata gathering and analysis system of claim 8, wherein analyzing themain log and the repeat log comprises: performing a strict test ofrepeatability based on a first correlation analysis of the main log andthe repeat log without any depth shift adjustment, wherein said firstcorrelation analysis generates, as part of the repeatability measure, acorrelation coefficient without depth shift.
 11. The data gathering andanalysis system of claim 10, the instructions, when executed, furthercausing the computer processor to: determine, by at least comparing thecorrelation coefficient without depth shift to a first pre-determinedminimum correlation threshold, a pass status or a fail status of thestrict test of repeatability, wherein the repeatability measure furthercomprises the pass status or the fail status of the strict test ofrepeatability.
 12. The data gathering and analysis system of claim 11,wherein analyzing the main log and the repeat log further comprises:performing, in response to determining the fail status of the stricttest of repeatability, a lenient test of repeatability based on a secondcorrelation analysis of the main log and the repeat log with an optimaldepth shift adjustment, wherein said second correlation analysisgenerates, as part of the repeatability measure, a depth shift dependentcorrelation coefficient.
 13. The data gathering and analysis system ofclaim 12, the instructions, when executed, further causing the computerprocessor to: determine, by at least comparing the depth shift dependentcorrelation coefficient to a second pre-determined minimum correlationthreshold, a pass status or a fail status of the lenient test ofrepeatability, wherein the repeatability measure further comprises thepass status or the fail status of the lenient test of repeatability. 14.The data gathering and analysis system of claim 12, wherein the optimaldepth shift adjustment corresponds to an optimal value of the depthshift dependent correlation coefficient.
 15. A system comprising: awellsite having a wellbore penetrating a subterranean formation in afield; and a data gathering and analysis system comprising functionalityfor: generating, by repeatedly performing well logging of a wellborepenetrating a subterranean formation in a field, a set of well log datafiles each comprising a plurality of data channels, each data channelcomprising a series of measurement data records representing a downholeproperty along a depth in the wellbore; analyzing, by a computerprocessor, a main log and a repeat log of the set of well log data filesto determine a repeatability measure of the set of well log data files;presenting, using a graphical user interface, the repeatability measureto a user; and facilitating, based on a user input in response topresenting the repeatability measure, the field operation.
 16. Thesystem of claim 15, the data gathering and analysis system furthercomprising functionality for: obtaining, using the graphical userinterface, a verification configuration file, wherein determining therepeatability measure of the set of well log data files is based on theverification configuration file.
 17. The system of claim 1, whereinanalyzing the main log and the repeat log comprises: performing a stricttest of repeatability based on a first correlation analysis of the mainlog and the repeat log without any depth shift adjustment, wherein saidfirst correlation analysis generates, as part of the repeatabilitymeasure, a correlation coefficient without depth shift.
 18. The systemof claim 17, the data gathering and analysis system further comprisingfunctionality for: determining, by at least comparing the correlationcoefficient without depth shift to a first pre-determined minimumcorrelation threshold, a pass status or a fail status of the strict testof repeatability, wherein the repeatability measure further comprisesthe pass status or the fail status of the strict test of repeatability.19. The system of claim 18, wherein analyzing the main log and therepeat log further comprises: performing, in response to determining thefail status of the strict test of repeatability, a lenient test ofrepeatability based on a second correlation analysis of the main log andthe repeat log with an optimal depth shift adjustment, wherein saidsecond correlation analysis generates, as part of the repeatabilitymeasure, a depth shift dependent correlation coefficient, and whereinthe optimal depth shift adjustment corresponds to an optimal value ofthe depth shift dependent correlation coefficient.
 20. The system ofclaim 19, the data gathering and analysis system further comprisingfunctionality for: determining, by at least comparing the depth shiftdependent correlation coefficient to a second pre-determined minimumcorrelation threshold, a pass status or a fail status of the lenienttest of repeatability, wherein the repeatability measure furthercomprises the pass status or the fail status of the lenient test ofrepeatability.